Studies of Big Data Processing at Linear Accelerator Sources Using Machine Learning

Bawatna, Mohammed; Green, Bertram

doi:10.1007/978-3-030-51971-1_37

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2022

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“…In linear accelerator sources, individual terahertz pulses' spectral form and temporal delay can be properly predicted using straight-forward machine learning algorithms [42]. On heterogeneous processors like FPGAs and GPUs, these algorithms can process a massive amount of data to train deep and special Artificial Neural Networks (ANN) [42].…”

Section: Related Workmentioning

confidence: 99%

A Survey of Machine Learning for Big Data Processing

Almutiri¹,

Alhabeeb²,

Alhumud³

et al. 2022

Journal on Big Data

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Today's world is a data-driven one, with data being produced in vast amounts as a result of the rapid growth of technology that permeates every aspect of our lives. New data processing techniques must be developed and refined over time to gain meaningful insights from this vast continuous volume of produced data in various forms. Machine learning technologies provide promising solutions and potential methods for processing large quantities of data and gaining value from it. This study conducts a literature review on the application of machine learning techniques in big data processing. It provides a general overview of machine learning algorithms and techniques, a brief introduction to big data, and a discussion of related works that have used machine learning techniques in a variety of sectors to process big amounts of data. The study also discusses the challenges and issues associated with the usage of machine learning for big data.

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Section: Related Workmentioning

confidence: 99%